Background Emergency medical service (EMS) personnel have high COVID-19 risk during resuscitation. The resuscitation protocol for patients with out-of-hospital cardiac arrest (OHCA) was modified in response to the COVID-19 pandemic. However, how the adjustments in the EMS system affected patients with OHCA remains unclear. Methods We analysed data from the Taichung OHCA registry system. We compared OHCA outcomes and rescue records for 622 cases during the COVID-19 outbreak period (1 February to 30 April 2020) with those recorded for 570 cases during the same period in 2019. Results The two periods did not differ significantly with respect to patient age, patient sex, the presence of witnesses or OHCA location. Bystander cardiopulmonary resuscitation and defibrillation with automated external defibrillators were more common in 2020 (52.81% vs 65.76%, p<0.001%, and 23.51% vs 31.67%, p=0.001, respectively). The EMS response time was longer during the COVID-19 pandemic (445.8±210.2 s in 2020 vs 389.7±201.8 s in 2019, p<0.001). The rate of prehospital return of spontaneous circulation was lower in 2020 (6.49% vs 2.57%, p=0.001); 2019 and 2020 had similar rates of survival discharge (5.96% vs 4.98%). However, significantly fewer cases had favourable neurological function in 2020 (4.21% vs 2.09%, p=0.035). Conclusion EMS response time for patients with OHCA was prolonged during the COVID-19 pandemic. Early advanced life support by EMS personnel remains crucial for patients with OHCA.

Objective: To describe the characteristics of children who present to an emergency department (ED) with facial palsy and determine the association of outcome with etiology, degree of initial paralysis, and ED management. Methods: This was a retrospective cohort study of children who presented to an ED with facial nerve paralysis (FNP). Results: There were 85 patients with a mean age of 8.0 (SD, 6.1) years; 60% (n = 51) of the patients were male, and 65.9% (n = 56) were admitted to the hospital. Bell palsy (50.6%) was the most common etiology followed by infectious (22.4%), traumatic (16.5%), congenital (7.1%), and neoplastic etiologies (3.5%). Patients with Bell palsy had shorter recovery times (P = 0.049), and traumatic cases required a longer time for recovery (P = 0.016). Acute otitis media (AOM)-related pediatric FNP had shorter recovery times than non-AOM-related cases (P = 0.005) in infectious group. Patients given steroid therapy did not have a shorter recovery time (P = 0.237) or a better recovery (P = 0.269). There was no difference in recovery rate of pediatric patients with Bell palsy between hospitalization or not (P = 0.952). Conclusion: Bell palsy, infection, and trauma were most common etiologies of pediatric FNP. Recovery times were shorter in pediatric patients with Bell palsy and AOM-related FNP, whereas recovery took longer in traumatic cases. Steroid therapy did not seem beneficial for pediatric FNP. Hospitalization is not indicated for pediatric patients with Bell palsy.

To summarize evidence for the diagnostic accuracy of procalcitonin (PCT) tests for identifying secondary bacterial infections in patients with influenza. Major databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies published between January 1966 and May 2009 that evaluated PCT as a marker for diagnosing bacterial infections in patients with influenza infections and that provided sufficient data to construct two-by-two tables. Six studies were selected that included 137 cases with bacterial coinfection and 381 cases without coinfection. The area under a summary ROC curve was 0·68 (95% CI: 0·64-0·72). The overall sensitivity and specificity estimates for PCT tests were 0·84 (95% CI: 0·75-0·90) and 0·64 (95% CI: 0·58-0·69), respectively. These studies reported heterogeneous sensitivity estimates ranging from 0·74 to 1·0. The positive likelihood ratio for PCT (LR+ = 2·31; 95% CI: 1·93-2·78) was not sufficiently high for its use as a rule-in diagnostic tool, while its negative likelihood ratio was reasonably low for its use as a rule-out diagnostic tool (LR- = 0·26; 95% CI: 0·17-0·40). Procalcitonin tests have a high sensitivity, particularly for ICU patients, but a low specificity for identifying secondary bacterial infections among patients with influenza. Because of its suboptimal positive likelihood ratio and good negative likelihood ratio, it can be used as a suitable rule-out test but cannot be used as a standalone rule-in test.

Bacteremia is a life-threatening complication of infectious diseases. Bacteremia can be predicted using machine learning (ML) models, but these models have not utilized cell population data (CPD). The derivation cohort from emergency department (ED) of China Medical University Hospital (CMUH) was used to develop the model and was prospectively validated in the same hospital. External validation was performed using cohorts from ED of Wei-Gong Memorial Hospital (WMH) and Tainan Municipal An-Nan Hospital (ANH). Adult patients who underwent complete blood count (CBC), differential count (DC), and blood culture tests were enrolled in the present study. The ML model was developed using CBC, DC, and CPD to predict bacteremia from positive blood cultures obtained within 4 h before or after the acquisition of CBC/DC blood samples. This study included 20,636 patients from CMUH, 664 from WMH, and 1622 patients from ANH. Another 3143 patients were included in the prospective validation cohort of CMUH. The CatBoost model achieved an area under the receiver operating characteristic curve of 0.844 in the derivation cross-validation, 0.812 in the prospective validation, 0.844 in the WMH external validation, and 0.847 in the ANH external validation. The most valuable predictors of bacteremia in the CatBoost model were the mean conductivity of lymphocytes, nucleated red blood cell count, mean conductivity of monocytes, and neutrophil-to-lymphocyte ratio. ML model that incorporated CBC, DC, and CPD showed excellent performance in predicting bacteremia among adult patients with suspected bacterial infections and blood culture sampling in emergency departments.

Background: Overcrowding in emergency departments (ED) is a critical problem worldwide, and streaming can alleviate crowding to improve patient flows. Among triage scales, patients labeled as “triage level 3” or “urgent” generally comprise the majority, but there is no uniform criterion for classifying low-severity patients in this diverse population. Our aim is to establish a machine learning model for prediction of low-severity patients with short discharge length of stay (DLOS) in ED.Methods: This was a retrospective study in the ED of China Medical University Hospital (CMUH) and Asia University Hospital (AUH) in Taiwan. Adult patients (aged over 20 years) with Taiwan Triage Acuity Scale level 3 were enrolled between 2018 and 2019. We used available information during triage to establish a machine learning model that can predict low-severity patients with short DLOS. To achieve this goal, we trained five models—CatBoost, XGBoost, decision tree, random forest, and logistic regression—by using large ED visit data and examined their performance in internal and external validation.Results: For internal validation in CMUH, 33,986 patients (75.9%) had a short DLOS (shorter than 4 h), and for external validation in AUH, there were 13,269 (82.7%) patients with short DLOS. The best prediction model was CatBoost in internal validation, and area under the receiver operating cha racteristic curve (AUC) was 0.755 (95% confidence interval (CI): 0.743–0.767). Under the same threshold, XGBoost yielded the best performance, with an AUC value of 0.761 (95% CI: 0.742- 0.765) in external validation.Conclusions: This is the first study to establish a machine learning model by applying triage information alone for prediction of short DLOS in ED with both internal and external validation. In future work, the models could be developed as an assisting tool in real-time triage to identify low-severity patients as fast track candidates.